Omniscope (AI-Driven Planning Tool) Revamp
Turning a powerful but overwhelming AI planning tool into a confident, efficient experience
Team
Vision Phase: 2 Product Designers, 1 Product Manager, 1 User Researcher
Execution Phase: 1 Product Designer, 1 Product Manager, 1 User Researcher, 2 Front-End Engineers
Timeline
Vision Phase: Oct 2024 - Dec 2024, 12 weeks
Execution Phase: Oct 2025 - Now
(Design: Oct 2025, 3.5 weeks; Development: Oct 2025 - Now)
What is Omniscope?
Omniscope is an AI-powered forecasting and planning tool that helps advertisers estimate reach, budget efficiency, and delivery outcomes before setting up a live campaign.
For example, if Nike is planning an ad campaign for a new shoe launch, what ad formats would be most effective and what reach can be realistically expected?
Omniscope uses AI/ML to answer these questions, helping advertisers make informed planning decisions.
So, What’s wrong with Omniscope?
Despite being our first and most powerful AI product, Omniscope struggled with low adoption. Over time, as more features were added, the workflow became increasingly overwhelming and fragmented.
New users in particular lacked confidence to get started and found it difficult to move smoothly from setup to insights.
As a key pillar in our AI strategy, Omniscope needed more than incremental improvements. A holistic experience revamp was required to unlock its true value.
BEFORE
What did I do?
A revamp of the existing experience was not originally on the roadmap. Seeing the growing gap between the tool’s potential and its real-world usage, I proactively partnered with a PM to propose this initiative.
Under tight time and resource constraints, I helped form a lean, cross-functional team and led the exploration of a new vision, grounding our direction in continuous validation with both internal and external users. Our focus was to clarify Omniscope’s value and streamline the workflow, lowering the barrier to entry and encouraging broader adoption.
After defining a clear direction, I socialized the vision with stakeholders, aligned it with broader product priorities, and translated it into achievable, shippable milestones. When engineering capacity became available, I worked closely with the team to rapidly turn the vision into a shipped MVP.
What we delivered:
Value-first Onboarding
Clear value at a glance while maintaining a familiar, tool-like experience
Simplified entry points, reducing 40+ actions to 3 clear ways to start
Flattened hierarchy to preview the range of possible outputs
Simplified, Guided Inputs
Reduced default targeting options from 35 to 11, focusing on what matters most
Progressive disclosure reveals options only when they are relevant and available
Live forecasting preview helps users validate inputs early and avoid invalid outputs
Enhanced Input-to-Output
Seamless drill-down connecting inputs to insights
Clearer information hierarchy and density across output data
Impact
This project is in beta testing phase and is expected to reach GA this quarter. While final product metrics are not yet available, early testing and stakeholder reviews already signal strong impact.
7
new external clients
onboard during beta testing
72%
reduced time
to reach a valid forecasting result
4.5/5
avg. score
on ease of use and visual refresh
It feels completely updated. It’s far more intuitive and user-friendly. Once you start working, everything—from navigation to insights—just makes sense”
— external users
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© Proudly created by Jiajun (Janet) Dai
janetd0908@gmail.com